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    "#An Introduction to Bias-Variance\n",
    "\n",
    "##Introduction\n",
    "\n",
    "<p>This notebook gives a foundational overview of the Bias-Variance tradeoff,  one of the most important topics related to predictive modeling for Data Science. The Bias-Variance Tradeoff in short is a framework for understanding where error comes from in a supervised learning model. There is a famous quote attributed to statistician George Box that goes:<br><br>\n",
    "\n",
    "<i><center>\"All models are wrong, though some are useful.</center></i><br>\n",
    "\n",
    "The word \"model\" alone implies some simplification of the world, and such simplifications generally accept a level of misrepresentation for the sake of parsimony (ie., simplicity). The challenge we face as Data Scientists is that the world we are usually trying to model produces data that is inherently noisy. Modeling such noisy systems requires a delicate balancing act between model parsimony and generalizability. When our measurement systems are rife with uncertainty, the desire to build simpler models isn't just a stylistic choice - it is an essential practice if we desire to successfully generalize on unknown data.<br><br>\n",
    "\n",
    "The Bias-Variance tradeoff is a theoretical concept that fortunately can be very intuitive and easy to illustrate. This notebook aims to offer the reader both views. On the theory side, we'll define and explain some core constructs and show how the common least squares error can be decomoposed into both bias and variance components. To make the concepts more intuitive, we'll present multiple illustrations with simulated data. The rest of this notebook is organized as follows:<br>\n",
    "\n",
    "\n",
    "<ul>\n",
    "    <li>Explore and define Bias and Variance with a focus on illustration.</li>\n",
    "    <li>Derive the Bias-Variance decomposition for least squares error.</li>\n",
    "    <li>Discuss practical considerations that result from the Bias-Variance decomposition.</li>\n",
    "</ul>\n",
    "</p>\n",
    "\n",
    "##Intuitive Explanation of Bias and Variance.\n",
    "<p>Both \"bias\" and \"variance\" are common terms in statistics, and their general meaning isn't that far from how they are used in statistical learning theory. If you recall in statistics, the bias of an estimator is defined as the difference between the expected value of an estimator and the true value of the quantity being estimated. The \"bias\" and \"variance\" of a predictive model follow the same idea since you can think of a model as a function that estimates the expected value of an outcome $Y$ conditional on some $X=x$,  (i.e., $f(x)=E[Y|X=x]$).\n",
    "\n",
    "<br><br>\n",
    "\n",
    "So to get closer to an intuitive explanation, let's start by looking at some simulated data. The following plots show a polymonial scatter created with the following data generating process:<br><br>\n",
    "\n",
    "<center>$Y=\\beta^T \\: K^d(X) + \\epsilon$</center><br>\n",
    "\n",
    "Where $X$ is a single numeric variable, $K^d(X)$ is a $d$-degree polynomial kernel on $X$ (i.e., $K^3(X)$ admits a vector $<X^3, X^2, X>$), $\\beta$ is a $d$-length coefficient vector and $\\epsilon$ is a normal random variable with $0$-mean and variance $\\sigma^2$.\n",
    "</p>"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104e0a450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"./utils/\")\n",
    "import numpy as np\n",
    "import bias_variance as bv\n",
    "reload(bv)\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "b = [-10, 2, -0.04, 0.000275]\n",
    "d = bv.simPolynomial(sigma = 20, betas = b, n = 40)\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(d['x'], d['y'], 'b.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The points above follow some sort of trend, but given the level of noise, it is hard to trace exactly what that trend is. Imagine you didn't know the data generating function and that someone gave you a piece of string and asked you to lay the string out so that it best captures the trend you see in the data. The string, once laid out, is your mental model of how this data was generated. Although a piece of string is infinitely flexible, imagine that you are required in advance to specify exactly how many times your piece of string is allowed to bend. If your only task was to draw a trend line that was as close to as many points above as possible, you'd likely choose the infinitely flexible option. Your game master here reveals though that there is another set of points hidden from you, and the real task is to have your string be as close to the hidden points as possible (which we'll assume will at least come from the same data generating process). With this revelation, you'd have to ask yourself whether you prefer a string with fewer flex points, that sits near a safe average of any cluster of points, or the infinitely flexible string that can possibly go through all points. This is the choice that every modeler has to make, and understanding both bias and variance will help guide us towards making better modeling choices.\n",
    "\n",
    "</p>\n",
    "\n",
    "####Deeper Look at Bias\n",
    "<p>\n",
    "Your choice will likely depend on how confident you are that the points you see really represent the world of such points. Your confidence in that department is likely proportional to the number of points you can see here. Even if you had an infinite number of points, the string with fewer flex points might never be able to trace the true trend in the data. We'll illustrate this with the following chart, using a straight line (string with zero flexibility) as our model.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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eopC84/b6ziqfet0poRhGNCTC7W6D16iLG4xC30fA+zAsfxj8x4LV3f7LGIye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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1093e6990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reload(bv)\n",
    "bv.plotLinearBiasStage(20, b, 5*np.array([2**i for i in range(12)]), (14, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The blue dots represent the data we are trying to model. Each line is a linear model fit to a different data set from the data generating model (same as above), but with increasingly more points. You can see that most of the lines cluster together, and that the line fit with the most points is right in the middle there. The starred, black-curve running through the middle is the true mean of the distribution (i.e., $E[Y|X]$ if you were to have infinitely many data points to model). We can see that even the linear model fit on the most data is far away from this true mean at various points. If we extended the simulation and modeled with infinitely large data sets, nothing would change this fact: a linear model will never truly fit the curvature of the data. This is an example of model bias. Because this is a regression problem, we can even quantify this bias: <br><br>\n",
    "\n",
    "A linear model will take the form:<br>\n",
    "<center>$E^{d=1}[Y|X=x]=\\hat{\\beta} *\\:x$</center><br>\n",
    "\n",
    "But we know that our data follows the form $Y=\\beta^T \\: K^d(X) + \\epsilon$ and a properly specified model would give the following estimate:<br><br>\n",
    "\n",
    "<center>$E[Y|X=x]=\\beta^T \\: K^d(X)$</center><br>\n",
    "\n",
    "The bias of the linear model is the difference between its estimate of $E^{d=1}[Y|X]$ and the true $E[Y|X]$, which is:<br><br>\n",
    "\n",
    "<center> Bias(d=1) = $|E^{d=1}[Y|X=x] - E[Y|X=x]| = |\\hat{\\beta} *\\:x - \\beta^T \\: K^d(X)|$</center><br>\n",
    "\n",
    "Bias is not a quantity that you can reduce by adding more data (directly at least), because it is a property of the type of model you have chosen a priori to fitting the data. The expecations above are take over the true probability distribution $P(X,Y)$ and have nothing to do with the actual training data sample (and specifically, the quantity of it). The consequence of this fact is that adding more data without changing the form of the model won't make any bias disappear (if it is to exist at all). Thus, can't reduce any error caused by bias if we can't change the model structure we are using. <br><br>\n",
    "\n",
    "<i>So why not just make models as flexible as possible?</i><br><br>\n",
    "\n",
    "This is a perfect segueway for discussing variance in model estimation.\n",
    "</p>\n",
    "\n",
    "####Deeper Look at Variance\n",
    "\n",
    "<p>\n",
    "\n",
    "Core machine learning theory focuses a lot of attention on how flexible a particular type of model can be. The best algorithms (in terms of accuracy) are usually the ones that can fit any arbitrary function that may represent the classification or regression task. Of course, with great power comes great responsibility. When we seek to reduce bias my using more sophisticated modeling algorithms, we have to be mindful that a lot of algorithms are really good at fitting the noise in our data. With smaller data sets, the models have a harder time distinguising inherent noise from signal. Variance in this context is thus intimately linked to the size of the data set.<br><br>\n",
    "\n",
    "An important concept for understanding model variance is to define the variance of 'what' over 'what.' Remember again that a model generally tries to estimate $E[Y|X]$, and we use a given training set $j$ to learn this estimate. Every training set though is just some sample over a larger, hypothetical distribution $D$ (that in practice we generally never know). Now imagine a set of parallel worlds, each having some different (but similar) dataset $j$ sampled from $D$.  Now further imagine that we estimate a different $E^j[Y|X]$ over each $D^j$ using some algorithm with a fixed structure. Because of the random permutations and differences of the different training sets, we should expect each $E^j[Y|X]$ to be slightly different than the next. Exactly how different is the appropriate question here. More flexible models will likely produce more varied estimates, and this variation will increase as sample sizes decrease. So in short, the variance we refer to here is $Var(E^j[Y|X])$, over possible different permutations of the data set. <br><br>\n",
    "\n",
    "The following illustrates this idea using the simulation example from above. In this case we compare a biased (linear) model to a zero-bias model with different sized training sets. We simulate the parallel world concept and plot the results of the model trained on each 'world.'\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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7IZJz3jH9yo6T3n294wD94iOVvsNL3n2p6h/N9cJ5bn+3WAlr1uSF2+TgILPX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\nIiIiIiIiIiIiIo31fxvmBKGvgQDiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113fba510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reload(bv)\n",
    "bv.plotVariance(20, b, [10, 50, 100], (15, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>This chart paints several trends around the idea of model variance. First though, let's arm ourselves by actually defining model variance. For a particular value of $X=x$:<br><br>\n",
    "\n",
    "<center> Model Variance = $E_D[(\\hat{Y}_x - E_D[\\hat{Y}_x])^2]$</center><br><br>\n",
    "\n",
    "For notational convenience, we define $\\hat{Y}_x = E_j[Y|X=x]$, where $j$ indicates a particular data set (i.e., this is the estimated model fit for a particular value of $X=x$ on a particular instance of the training data $j$). The notation $E_D[.]$ means we are taking an expectation over all possible datasets $j$ of size $N$ that are sampled from $D$. In each chart above, the red line is the avg. of $\\hat{Y}_x$, which is an unbiased and consistent estimator of $E_D[\\hat{Y}_x]$. Each grey line represents the model estimated from each training set $j$ in our simulation. The quantity $E[std|X]$ represented in the title is the avg. standard deviation (square root of variance) across all values of $X$ in the data. <br><br>\n",
    "\n",
    "With the core concepts and charts defined, we can focus on the interesting trends shown above. Visually we can see that the variance increases as we switch from the biased linear model (top row) to the unbiased polynomial model (bottom row). We can also see that the variance decreases left to right for both model classes, which is caused by adding more data to each model. So in short, model variance tends to increase under two scenarios: 1) the model you are fitting is more flexible or complex, 2). sample sizes decrease. Intuitively, this should make sense. More flexible models can essentially go through more points. The problem though, is that a lot of the points are just noise, and so in any one realization of the data, the model has less evidence to distinguish whether the point is signal or noise. A biased and inflexible model doesn't care as much about noise - it doesn't have what it takes to fit the noise. <br><br>\n",
    "\n",
    "One subtle but important item to point out here is that the dispersion of the models (grey lines) around their average is not equal for all values of $X$. In particular, we can see more dispersion around extreme points. If you recall, the data generating process had an error term with equal variance for each value of $X$ (homoscedasticity is what we call that). So how do we end up with extra variance at the extreme values? This is a subtle but important point that happens a lot in modeling. In most cases, data is not uniformly distributed around $X$, so extreme ranges tend to have less support. Since we defined model variance above conditioned on $X=x$, what matters more is not the total sample size, but the number of instances where $X=x$. It turns out that model variance is often higher where $P(X=x)$ is lower. This technically doesn't explain the results above though, because the data was generated uniformly random on $X$. In a related, but different way, the extreme values have more variance because each extreme value has fewer points near it. Parametric models (which is what we have here) \"borrow\" information from neighboring points to get a good fit. When a point has fewer neighbors (which is the case at the boundaries), there are fewer neighbors to exploit. <br><br>\n",
    "\n",
    "So how much data is required to reduce or at least stabilize the variance? The exact sample size is heavily problem, model and data dependent, but we can at least extend the above simulation to show the basic trend. \n",
    "\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LkSRJklSojjPRdC+LsCGwcprrztTcuJxDI4H3A3sDzydQLrocSZIkSYXoOBON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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1094ceb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reload(bv)\n",
    "res = bv.getVarianceTrend(20, b)\n",
    "bv.plotVarianceTrend(res, (15, 9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>This plot should confirm several of our expectations. First, when the complexity of our models increase (measured by the degree of the polynomial), model variance increases. Also, variance decreases as we increase the sample size. Beyond that, note the shape of the curves in the top plot above (for any model degree). We can see that doubling the data (each tick on the x-axis represents a doubling) does not halve the variance. For instance, with $d = 4$ (our one unbiased model family), going from $2^10$ to $2^11$ records drops the variance around 50%. The bottom chart shows both the sample size and model on the log2 scale. We can see that relationship on the log-log scale is nearly perfectly linear. Every unit increase in log2 scale decreases the log2 of the variance by a constant amount, such that: $log_2(\\hat{\\sigma_m}^2) =  -log_2(N) + c$. With a little algebra we get to $\\hat{\\sigma_m}^2 \\propto N^{-1}$. Here we have shown empirically what is well known analytically - that model variance reduces at the rate of $O(N^{-1})$. For reasons that we'll discuss later, investing in more and more data to reduce the variance doesn't always pay off (in terms of reducing error to balance out the additional data cost).\n",
    "</p>\n",
    "\n",
    "##Model Error, Bias and Variance\n",
    "\n",
    "<p>So at this point we should have a solid foundation as to what exactly we mean by a model's bias and variance. And most importantly, we should now be able to understand the drivers of model bias and variance. Understanding such drivers gives the modeler an element of control over the two phenomena. But why do we need to control such things? After all, our modeling goal is generally not to hit some target level of bias or variance. Rather, we want to reduce the prediction error of our model. It turns out, bias and variance are intimately connected to model error, such that the total model error is often explicitly determined by the amount of bias or variance. Thus, controlling bias and variance in essence helps us control the error.\n",
    "</p>\n",
    "\n",
    "###Bias-Variance Decomposition for Least-Squares Error\n",
    "\n",
    "<p>\n",
    "The most famous and convenient representation of this relationship is done using least-squares error. For problems in which MSE (least squares) is our error function, we can express the MSE explicitly in terms of model bias, model variance and the underlying noise in our target data (which is often called the 'irreducible error', for reasons discussed later on). In this section we'll derive this famous decomposition.<br><br>\n",
    "\n",
    "<b>Important Concepts Defined</b><br><br>\n",
    "\n",
    "First, let's assume our data comes in pairs $(X, Y)$, and our general estimation goal is to learn a function $f(X)$ such that $f(x) = E[Y|X=x]$. Because our data is finite, we never know the true $E[Y|X=x]$, but we can estimate $f_j(x) = \\hat{Y}_x = E_j[Y|X=x]$ over some fixed, finite sample $j$.  Our MSE at a give value of $X=x$, with known $Y=y$ is defined as:\n",
    "\n",
    "<center>$MSE(x, y) = (y - \\hat{Y}_x)^2$</center><br><br>\n",
    "(note, for notational simplicity we ignore the fact that MSE is often computed and averaged across all instances of the data. The derivation still holds for a single instance). <br><br>\n",
    "\n",
    "Before we continue, we need to introduce and explain some important concepts and quantities. <br><br>\n",
    "\n",
    "<ul>\n",
    "    <li><u>Family of functions $\\mathbb{F}$</u>: This is a finite set of functions that share some structural properties. For instance, all decision trees on a set of features with depth=10, or all linear functions of the form $f(X)=\\beta^T \\: X$. Generally any classification or algorithm can be placed into some particular family $\\mathbb{F}$.</li><br>\n",
    "\n",
    "    <li><u>Expected vs. Empirical risk:</u>The expected risk is a theoretical property we could know if we knew the true distribution $P(X,Y)$. For MSE the expected risk of a given function $f$ is $E[MSE(f)]= \\int ((y-f)^2 \\: dP(X,Y)$. Since we generally don't know $P(X,Y)$, we have to approximate the expected risk with the empirical risk, which is defined as: $E_j[MSE(f)] =n^{-1}\\sum ((y_i - f_i)^2$, where the sum is taken over some finite data set $j$.</li><br>\n",
    "\n",
    "    <li><u>The true risk minimizer $f^*$</u>: This is the function $f^*$ that best approximates the true $E[Y|X=x]$ and is the one that minimizes the expected risk across all families of functions. It can be defined as: $f^*= \\underset{f} {\\mathrm{argmin}} \\int((Y-f)^2 \\: dP(X,Y)$. In other words, this is the absolute truth that we wish we knew, but generally have to approximate using a finite data sample and some estimation procedure.</li><br>\n",
    "\n",
    "    <li><u>The expected risk minimizer from family $\\mathbb{F}$,   $\\:\\:f^{\\mathbb{F}}$</u>:This is similar to the true risk minimizer, only that we restrict the exploration of candidate functions to only those included in $\\mathbb{F}$. The exact definition is: $f^{\\mathbb{F}}= \\underset{f \\in \\mathbb{F}} {\\mathrm{argmin}} \\int((Y-f)^2 \\: dP(X,Y)$. One way to think about this is, using the above example, imagine we had infinite data but could only fit some non-linear data scatter with a linear model - the result wold be the expected risk minimizer from the family of linear functions on $X$.</li><br> \n",
    "\n",
    "    <li><u>The emprical risk minimizer from family $\\mathbb{F}$,   $\\:\\:f_j^{\\mathbb{F}}$</u> This is similar to the above definition, but using the empirical risk definition as opposed to the expected risk definition. When we fit a model on finite data, this is generally the function we end up with.</li>\n",
    "</ul><br><br>\n",
    "\n",
    "\n",
    "<b>Derivation of MSE Bias-Variance Decomposition</b><br><br>\n",
    "\n",
    "So now we have 3 functions defined, $f^*$, $f^{\\mathbb{F}}$ and $f_j^{\\mathbb{F}}$. We'll first rewrite MSE using this new notation:$MSE = (y - f_j^{\\mathbb{F}})^2$. Also, as our goal is to generalize to new data, we care more about the expected MSE, or $E[(Y - f_j^{\\mathbb{F}})^2]$. What we'll now do is use algebra and some statistical rules to decompose this expected MSE into more familliar terms:<br><br>\n",
    "\n",
    "<center>$E[(Y - f_j^{\\mathbb{F}})^2] = E[Y^2] - 2E[Y*f_j^{\\mathbb{F}}] + E[(f_j^{\\mathbb{F}})^2]$</center><br><br>\n",
    "\n",
    "Using the rule that $E[X^2] = Var[X] + E[X]^2$, and a little algebra, we can expand this to:<br><br>\n",
    "\n",
    "<center>$E[Y^2] - 2E[Y*f_j^{\\mathbb{F}}] + E[(f_j^{\\mathbb{F}})^2] = Var[Y]+E[Y]^2 + Var[f_j^{\\mathbb{F}}]+E[f_j^{\\mathbb{F}}]^2- 2E[Yf_j^{\\mathbb{F}}] = Var[Y]+ Var[f_j^{\\mathbb{F}}] + (E[Y] - E[f_j^{\\mathbb{F}}])^2$</center><br><br>\n",
    "\n",
    "Now, according to our definitions above $Y = f^* + \\epsilon$, giving us $E[Y] = E[f^*] = f^*$., and $E[f_j^{\\mathbb{F}}] = f^{\\mathbb{F}}$ (remember, the expectations are taken over the true distribution so these terms are deterministic). Also, since $f^*$ is deterministic and independent of $\\epsilon$, $Var[Y] = Var[f^*] + Var[\\epsilon] = \\sigma^2$. Putting this all together, we get <b>the punchline of all of the above:</b><br><br>\n",
    "\n",
    "<center>$E[(Y - f_j^{\\mathbb{F}})^2] = \\sigma^2 + E[(f_j^{\\mathbb{F}} - f^{\\mathbb{F}})^2] + (f^*-f^{\\mathbb{F}})^2 = Irreducible Error + Variance + Bias^2$</center><br><br>\n",
    "\n",
    "Getting to this point was quite a feat, but now we have a great framework for understanding how and why our models may be wrong. Note that although we were able to derive an exact analytic bias-variance decomposition for MSE, other loss-functions (LogLoss, MAE, etc.) don't provide us with the same luxury. Nonetheless, the general framework is the same, and what we learn qualitatively from the MSE case can be applied to the Non-MSE case.\n",
    "</p>\n",
    "\n",
    "##Improving Models Using the Bias-Variance Framework\n",
    "\n",
    "<p>Understanding the bias-variance decomposition isn't just a theoretical exercise. Quite the opposite, it is the most fundamental principal that governs model generalizability and its lessons are directly applicable to empirical problems. Here are some key takeaways that we can highlight based on the above analysis that can be directly applied to our work. <br><br>\n",
    "\n",
    "<b>More data is always better:</b><br><br>\n",
    "This is probably an obvious statement based on everything we hear about \"Big Data,\" but now we have a framework to understand why. Having more data examples reduces model estimation variance at the rate of $O(N^{-1})$, which all else being fixed (remember, more data doesn't change the bias or irreducible error given a fixed model family), reduces the total error. An important caveat is that the value of this variance reduction might not be proportional to the costs associated with increased data. This is a very problem specific trade-off that should at least always be considered (remember, data costs can be in actual economic currency or CPUs). \n",
    "\n",
    "<b>Model complexity is your friend, but it can stab you in the back if you are not careful:</b><br><br>\n",
    "The bias part of the error decomposition is purely a function of your model specification and is independent of the data (specifically, the number of examples, not features, in your data). One can often get better results by using more complex (flexible) modeling algorithms. Example ways to add model complexity are: adding new features to the dataset, using less regularization in linear models, adding non-linear kernel functions to SVMs, using Neural Networks or Decision Tree based methods over linear methods. <br>\n",
    "\n",
    "The major caveat is worthy of its own paragraph here. Given a finite data set, adding more complexity will almost always increase the estimation variance of your model. As discussed above, more flexible models will confuse more noise (the $\\epsilon$ part of $Y=f^*+\\epsilon$) for signal. Thus, we have to be very careful here. Adding more data given a fixed model family might only waste time, money or CPUs, but it won't make your model worse. Adding more complexity can really hurt you if not done carefully. So this begs the questions:<br>\n",
    "\n",
    "<i>How much model complexity should I use?</i>\n",
    "\n",
    "<ul>\n",
    "<li>This is ultimately an empirical question, as the truth is never known outside of carefully crafted simulations. This is why we ALWAYS use train/test splits or cross-validation to guide our model selection. While we have a good analytic model for knowing how increased sample size will decrease variance, we don't have the same for predicting how increased complexity will increase it. Thus, we have to carefully test our models on out-of-sample data to find the empirically optimal trade-off. <br>\n",
    "</li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "<i>How do I know if my model is suffering from too much bias or too much variance.</i> <br>\n",
    "<ul>\n",
    "<li>The best performing model on out-of-sample data again will be the one that is 'just-right' in terms of its bias and variance. Generally, the sweet-spot will be the level of complexity in which the testing error is the lowest. Given a particular model fit, we can diagnose whether it suffers from high/low bias/variance buy comparing both the training and the testing error. If the training error and testing errors are both high, then bias is the likely culprit. If training error is low but testing error is high, then variance is likely the culprit. The following chart (taken from the book Elements of Statistical Learning 2) shows a hypothetical example. In a realistic case, the x-axis will be defined by whatever parameters define complexity for your choice of algorithms (i.e., the regularization weight, depth of trees, number of trees in a forest, etc.).\n",
    "\n",
    "\n",
    "</li>\n",
    "</ul>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CFKBAJQgwIK8EdBZJAQpQgAIUoAAFKEABClCAAhRgQM7fAQpQgAIUoAAFKEAB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j54RgMwETMIFCIPD222+jQYMGOPpo5TLZopHArifjaGyd22QCJlB0BFJW4afhP4YertWIWk17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\nlXdzdbUIC7MpF9nZIRg8djweursf6np65In/vNstHjN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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='references/bv_complexity_es2.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p>\n",
    "<b>In most observed phenomena, you can never get perfect predictions. Also, you shouldn't really compare models across different problems</b><br><br>\n",
    "\n",
    "These are two different statements, but they're both related to that $\\sigma^2$ term, which again we call the irreducible error. The first statement should be obvious if we assume $\\sigma>0$. Almost all systems we attempt to model, especially systems that capture human dynamics, are burdened with noise. With an infinite sample size we can potentially test every possible function and arrive at both zero variance and zero bias. Even then, the quantum mechanical nature of the universe, and the fact that there is always likely some unmeasured variable (whose effect can be folded into $\\epsilon$), means that $Y$ will always have some unexplainable variance. No model and modeler will ever get beyond that point (for those interested, this error is often called the 'Bayes Error').<br><br>\n",
    "\n",
    "The second statement above is related to the fact that every $Y^p$ will have an associated $\\epsilon^p$. A model predicting $E[Y^1|X=x]$ may have both lower bias and variance than a model predicting $E[Y^2|X=x]$, but if $Var[\\epsilon^1]>Var[\\epsilon^2]$, the model on $Y^1$ can still have a worse error than the one for $Y^2$. Generally this isn't a problem, so long as the appropriate comparisons are made. Specifically, the qualitative assessment of a model should be relative to a baseine derived from the same problem (i.e., predicting the same $Y^p$). And in some circumstances, a model that does 5% better than random on some problem may be worth a lot more money than a model that is nearly perfect (think about predicting stocks vs. predicting sunset). <br><br>\n",
    "\n",
    "Corrollary to the above paragraph is the following rule: don't judge a model just because its error is too close to random. Again, every model should be judged using appropriate baselines that are specific to the problem at hand. Accuracy might be far from 100%, but the model can still be good (and this relates back to how much irreducible error is in the underlying system).\n",
    "</p>\n",
    "\n",
    "##Wrap Up\n",
    "\n",
    "<p>At this point you should (hopefully) understand the following at both an intuitive level and foundational theoretical level:\n",
    "\n",
    "<ul>\n",
    "    <li>Model Bias</li>\n",
    "    <li>Model Variance</li>\n",
    "    <li>The relationship between bias, variance and prediction error</li>\n",
    "    <li>How to simulate and represent graphically model bias and variance</li>\n",
    "    <li>How to use the bias-variance tradeoff to guide your modeling decisions</li>\n",
    " </ul><br>\n",
    "\n",
    "<b>references</b><br><br>\n",
    "\n",
    "The presentation here was intentionally light on theory and mainly focused on the bias-variance decomposition of MSE. For further exploration of the topic, feel free to consult the following:<br><br>\n",
    " \n",
    "<ul>\n",
    "    <li><a href=\"https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff\">Wikipedia</a></li>\n",
    "    <li><a href=\"http://statweb.stanford.edu/~tibs/ElemStatLearn/\">The Elements of Statistical Learning 2 (section 2.9, chapter 7)</a></li>\n",
    "    <li><a href=\"http://papers.nips.cc/paper/3323-the-tradeoffs-of-large-scale-learning.pdf\">Leon Bottou's work on the Tradeoffs of Large Scale learning (section )</a></li>\n",
    "    <li><a href=\"http://homes.cs.washington.edu/~pedrod/papers/mlc00a.pdf\">Pedro Domingo's General Framework for the Bias Variance Tradeoff</a></li>\n",
    "</ul>\n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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